JOSS paper

.github/workflows/draft-pdf.yml

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+on: [push]
+
+jobs:
+  paper:
+    runs-on: ubuntu-latest
+    name: Paper Draft
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Build draft PDF
+        uses: openjournals/openjournals-draft-action@master
+        with:
+          journal: joss
+          # This should be the path to the paper within your repo.
+          paper-path: docs/joss/paper.md
+      - name: Upload
+        uses: actions/upload-artifact@v1
+        with:
+          name: paper
+          # This is the output path where Pandoc will write the compiled
+          # PDF. Note, this should be the same directory as the input
+          # paper.md
+          path: docs/joss/paper.pdf
+        # These lines have been added following
+        # https://github.com/openjournals/openjournals-draft-action/issues/15
+      - name: save pdf to repo
+        uses: stefanzweifel/git-auto-commit-action@v4
+        with:
+          commit_message: Saved new PDF of paper

.gitignore

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 # no video outputs
 *.mp4
 
+# JOSS paper
+docs/joss/media
+docs/joss/paper.jats
+
 # not sure where they are from
-default.profraw
+default.profraw

docs/joss/paper.md

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+---
+title: 'SpeedyWeather.jl: Reinventing atmospheric general circulation models towards interactivity and extensibility'
+
+tags:
+  - Julia
+  - weather
+  - climate
+  - general circulation model
+  - spectral
+  - spherical harmonic transform
+
+authors:
+  - name: Milan Klöwer
+    orcid: 0000-0002-3920-4356
+    email: [email protected]
+    corresponding: true # (This is how to denote the corresponding author)
+    affiliation: "1, 2" # (Multiple affiliations must be quoted)
+
+  - name: Maximilian Gelbrecht
+    orcid: 0000-0002-0729-6671
+    affiliation: "3,4"
+
+  - name: Daisuke Hotta
+    orcid: 0000-0003-2287-0608
+    affiliation: "5,6"
+
+  - name: Justin Willmert
+    orcid: 0000-0002-6452-4693
+    affiliation: 7
+
+  - name: Simone Silvestri
+    orcid: 0000-0002-7156-946X
+    affiliation: 1
+
+  - name: Gregory L Wagner
+    orcid: 0000-0001-5317-2445
+    affiliation: 1
+
+  - name: Alistair White
+    orcid: 0000-0003-3377-6852
+    affiliation: "3,4"
+
+  - name: Sam Hatfield
+    orcid: 0000-0001-7235-6450
+    affiliation: 6
+
+  - name: Tom Kimpson
+    orcid: 0000-0002-6542-6032
+    affiliation: "2,8"
+
+  - name: Navid C Constantinou
+    orcid: 0000-0002-8149-4094
+    affiliation: 8
+
+  - name: Chris Hill
+    affiliation: 1
+
+affiliations:
+ - name: Massachusetts Institute of Technology, Cambridge, MA, USA
+   index: 1
+ - name: University of Oxford, UK
+   index: 2
+ - name: Technical University of Munich, Germany
+   index: 3
+ - name: Potsdam Institute for Climate Impact Research, Germany
+   index: 4
+ - name: Japan Meteorological Agency, Tsukuba, Japan
+   index: 5
+ - name: European Centre for Medium-Range Weather Forecasts, Reading, UK
+   index: 6
+ - name: University of Minnesota, Minneapolis, MN, USA
+   index: 7
+ - name: The University of Melbourne, Parkville, VIC, Australia
+   index: 8
+
+date: 27 March 2024
+bibliography: paper.bib
+
+---
+
+
+# Summary
+
+SpeedyWeather.jl is a library to simulate and analyze the global atmospheric
+circulation on the sphere. It implements several 2D and 3D
+models which solve different sets of equations:
+
+- the primitive equations with and without humidity (\autoref{fig:primitive}),
+- the shallow water equations (\autoref{fig:swm}), and
+- the barotropic vorticity equation.
+
+The primitive equation model in SpeedyWeather.jl is an
+atmospheric general circulation model [@Kucharski2013] with simple parameterizations
+for unresolved physical processes including precipitation or boundary layer mixing.
+It can be thought of as a conceptual reinvention of the Fortran SPEEDY model [@Molteni2003]
+in the Julia programming language [@Bezanson2017]. However, all models here are written in a modular
+way to make its components easily extensible. For example, a new parameterization can
+be externally defined and passed as an argument to the model constructor.
+Operators used inside SpeedyWeather.jl are exposed to the user,
+facilitating analysis of the simulation data.
+SpeedyWeather.jl is therefore, beyond its main purpose of simulating 
+atmospheric motion, also a library for the analysis of gridded data on the sphere.
+Running and analyzing simulations can be interactively combined, enhancing user
+experience and productivity.
+
+The user interface of SpeedyWeather.jl is heavily influenced by
+the Julia ocean model Oceananigans.jl [@Ramadhan2020].
+A monolithic interface [@Mazlami2017], controlling most of the model's functionality
+through arguments of a single function or through parameter files (often called namelists in Fortran),
+is avoided in favor of a library-style interface.
+A model is constructed bottom-up by first defining the discretization
+and any non-default model components with their respective parameters.
+All components are then collected into a single model object which, once
+initialized, returns a simulation object. A simulation contains everything,
+the model with all parameters as constructed before but also all prognostic and diagnostic variables.
+Such a simulation can then be run, but also accessed before and after to analyze or
+visualize the current variables, or individual terms of the equations.
+One can also adjust some parameters before resuming the simulation.
+While these steps can be written into a script for reproducibility,
+the same steps can be executed and interacted with one-by-one in
+Julia's read-evaluate-print loop (REPL) or in a Jupyter or Pluto notebook.
+We thereby achieve an interactivity of a simulation and its various model components
+far beyond the options provided in a monolithic interface.
+At the same time, defaults, set to well-established test cases, 
+enable even inexperienced users to run simulations in just a few lines of code. 
+
+![Surface humidity, air temperature, wind speed and precipitation simulated
+with the primitive equation model in SpeedyWeather.jl. Spectral resolution is
+T127 (about 100km) on an octahedral Gaussian grid [@Malardel2016] with simple
+physics to represent unresolved processes such as surface fluxes including
+evaporation, and precipitation due to large-scale condensation and convection.
+\label{fig:primitive}](primitive.png)
+
+SpeedyWeather.jl relies on Julia's multiple dispatch programming paradigm [@Bezanson2017]
+to be extensible with new components including parameterizations, forcing, drag,
+or even the grid.
+All such supported model components define an abstract type that can be
+subtyped to introduce, for example, a new parameterization.
+To define a new parameterization for convection in a given vertical column of the atmosphere,
+one would define `MyConvection` as a new subtype of `AbstractConvection`.
+One then only needs to extend the `initialize!` (executed once during model initialization)
+and `convection!` (executed on every time step)
+functions for this new type. Passing on `convection = MyConvection()`
+to the model constructor then implements this new model component without
+the need to branch off or overwrite existing model components.
+Conceptually similar scientific modelling paradigms have been very successful
+in the Python-based generic partial differential equation solver Dedalus [@Burns2020],
+the process-oriented climate model CLIMLAB [@Rose2018],
+and the Julia ocean model Oceananigans.jl [@Ramadhan2020].
+
+The dynamical core of SpeedyWeather.jl uses established numerics
+[@Bourke1972; @Hoskins1975; @Simmons1978; @Simmons1981],
+widely adopted in numerical weather prediction. It is based on the spherical
+harmonic transform [@reinecke2013; @stompor2011] with a leapfrog-based semi-implicit
+time integration [@Hoskins1975] and a Robert-Asselin-Williams filter [@Williams2011; @Amezcua2011].
+The spherical harmonic transform is grid-flexible [@Willmert2020]. Any iso-latitude ring-based
+grid can be used and new grids can be externally defined and passed in
+as an argument. Many grids are already implemented: the conventional
+Gaussian grid, a regular longitude-latitude grid, 
+the octahedral Gaussian grid [@Malardel2016], the octahedral
+Clenshaw-Curtis grid [@Hotta2018], and the HEALPix grid [@Gorski2005].
+Both SpeedyWeather.jl and its spherical harmonic transform are also
+number format-flexible. Single-precision floating-point numbers
+(Float32) are the default as adopted by other modelling efforts [@Vana2017; @Nakano2018],
+but Float64 and other custom number formats can be used with a single
+code basis [@Klower2022; @Klower2020].
+Julia will compile to the choice of number format, the grid,
+and and other model components just-in-time. A simple parallelization
+(across vertical layers for the dynamical core, across horizontal grid points
+for the parameterizations) is supported by Julia's multithreading.
+No distributed-memory parallelization is currently supported,
+GPU support is planned.
+
+SpeedyWeather.jl internally uses three sub-modules `RingGrids`,
+`LowerTriangularMatrices`, and `SpeedyTransforms`. `RingGrids` is a module that discretizes
+the sphere on iso-latitude rings and implements interpolations between various such grids.
+`LowerTriangularMatrices` facilitates the implementation of the spherical harmonics by organizing
+their coefficients in a lower triangular matrix representation.
+`SpeedyTransforms` implements the spectral transform between
+the grid-point space as defined by `RingGrids` and the spectral space defined in
+`LowerTriangularMatrices`. These three modules are independently usable
+and therefore support SpeedyWeather's library-like user interface.
+Output is stored as NetCDF files using NCDatasets.jl[@NCDatasets].
+
+![Relative vorticity simulated with the shallow water model in SpeedyWeather.jl.
+The simulation used a spectral resolution of T1023 (about 20 km) and Float32
+arithmetic on an octahedral Clenshaw-Curtis grid [@Hotta2018]. Relative vorticity
+is visualized with Matplotlib [@Hunter2007] and Cartopy [@Cartopy] using a
+transparent-to-white colormap to mimic the appearance of clouds. Underlaid is
+NASA's blue marble from June 2004. \label{fig:swm}](swm.png)
+
+# Statement of need
+
+SpeedyWeather.jl is a fresh approach to atmospheric models that have been
+very influential in many areas of scientific  and high-performance computing
+as well as climate change mitigation and adaptation.
+Most weather, ocean and climate models are written in Fortran
+(e.g. ICON [@ICON], CESM [@CESM], MITgcm [@MITgcm], NEMO [@NEMO]) and have been
+developed over decades. From this tradition follows a specific programming
+style and associated user interface.
+SpeedyWeather.jl aims to overcome the constraints of traditional Fortran-based models.
+The modern trend sees simulations in Fortran and data analysis in Python
+(e.g. NumPy [@Numpy], Xarray [@Xarray], Dask [@Dask], Matplotlib [@Hunter2007]),
+making it virtually impossible to interact with various model components directly.
+In SpeedyWeather.jl, interfaces to the model components are exposed to the user.
+Furthermore, data-driven climate modelling [@Rasp2018; @Schneider2023],
+which replaces existing model components with machine learning,
+is more difficult in Fortran due to the lack of established machine learning
+frameworks [@Meyer2022a]. 
+In Julia, Flux.jl [@Innes2019] is available for machine learning as well as automatic
+differentiation with Enzyme [@Moses2020] for gradients-based optimization.
+
+With SpeedyWeather.jl we hope to provide a platform for data-driven
+atmospheric modelling and in general an interactive model that makes difficult
+problems easy to simulate. Climate models that are user-friendly, trainable,
+but also easily extensible will suddenly make many complex
+research ideas possible.
+
+# Acknowledgements
+
+We acknowledge contributions from David Meyer, Mosè Giordano, Valentin Churavy, and Pietro Monticone
+who have also committed to the SpeedyWeather.jl repository, and the wider Julia community
+for help and support.
+MK acknowledges funding from the National Science Foundation.
+MK and TK acknowledge funding from the European Research Council
+under the European Union's Horizon 2020 research and innovation programme for the ITHACA grant (no. 741112).
+NCC acknowledges support by the Australian Research Council DECRA Fellowship DE210100749.
+
+# References